In this article Why the Turing Test Refuses to Retire Inside the Study: 72,000 Judgments, 1,916 Judges, 37 Models Barely Above Chance: What the Turing Test Scores Actually Mean When Interrogation Works: The Live-Chat Experiments Why Follow-Up Questions Trip Up the Turing Test’s Best Imitations The Fine Print: Limits Every Reader Should Keep in Mind […]
Two Stanford-led studies for COLM 2026 test AI benchmarks the way psychologists test exams. Across 56 benchmarks and 53 models, safety tests with the same label often disagreed, capability tests blurred together, and the scoring format mattered more than the topic. We explain the findings and what they mean for choosing a model.
A single testing mistake at Irregular, the Israeli AI security startup formerly known as Pattern Labs, explains rogue AI incidents disclosed separately by Anthropic, OpenAI, Meta and Google. We explain the two errors, each lab’s incident, the disclosure gaps, what Irregular says it has fixed, and the questions every buyer of AI testing should now ask.
Two McGill researchers argue that pacing frontier AI only works if the extra time is spent on anticipatory thinking. We check their case against the summer’s agent breaches involving OpenAI, Anthropic, Meta and the UK AI Security Institute, and show how teams can turn possible failures into tests.
A practical reference for the four families of AI agent evaluation metrics: accuracy, cost, safety and reliability. Includes computable definitions, starter thresholds, evaluation-set design, LLM-as-judge validation, and how to wire the numbers into a release gate.
A practical playbook for testing AI agents before launch: build a real evaluation set, red-team prompt injection and tool abuse, prove reliability under load, and gate every release on objective checks.
Why AI Agents Give Inconsistent Results is explained by the complete agent system, including instructions, retrieved information, memory, tools, permissions, orchestration, and runtime conditions.
A practical explanation of why claims about AI worldview convergence weaken when high-dimensional embeddings, cosine similarity, hubness, and weak baselines skew similarity scores.
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